Computers have become integral tools used in a wide variety of different applications, such as in finance and commercial transactions, computer-aided design and manufacturing, health care, telecommunication, education, etc. Computers are finding new applications as a result of advances in hardware technology and rapid development in software technology. Furthermore, the functionality of a computer system is dramatically enhanced by coupling these types of stand-alone devices together in order to form a networking environment.
It is understood that computer products such as laptop (notebook) computers and desktop computers are configurable products that may include a wide variety of interchangeable components. For example, the interchangeable computer components may include microprocessors of varying operating speeds, hard drives of different memory capacities, and optical drives of different disc formats such as compact disc (CD), CD-ReWriteable (CD-RW), digital versatile disc (DVD) or Combination, to name a few. Therefore, a computer may be considered a product package that encompasses a combination of components.
It is desirable for product managers and designers of computer systems to know the implicit price or value that their customers place on each component or product feature of their computer packages. One conventional solution for obtaining the implied component prices is to utilize surveys or questionnaires with computer customers. However, there are disadvantages associated with this conventional solution. For example, utilizing surveys or questionnaires can be costly to implement, can be time consuming, and often contain subjective biases which can be due to the small sample nature of the process.
Another conventional solution for obtaining the implied component prices of computer systems is to utilize the Hedonic Price Analysis approach proposed by economists. However, this conventional solution also has disadvantages associated with it. For example, the Hedonic Price Analysis might produce counterintuitive results, such as a slower processor to have a higher implicit price value than a faster processor, due to statistical variations and errors, which may cause those unfamiliar with statistical methodology, such as product managers and designers, to distrust this approach.
A third conventional solution for obtaining the implied component prices of computer systems is to utilize the Bayesian based approaches for Hedonic Price Analysis. But there are disadvantages associated with them as well. For example, these approaches are heavily mathematics/statistics oriented, involving sophisticated modeling, and apply to certain areas of applications. More importantly, these approaches can also produce counterintuitive results which may cause those unfamiliar with statistical methodology to distrust these approaches.
The present invention may address one or more of the above issues.